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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recurrent Neural Networks Models\n",
    "In this notebook, we show an example of how RNNs can be used with darts.\n",
    "If you are new to darts, we recommend you first follow the [quick start](https://unit8co.github.io/darts/quickstart/00-quickstart.html) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import shutil\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.models import RNNModel, ExponentialSmoothing, BlockRNNModel\n",
    "from darts.metrics import mape\n",
    "from darts.utils.statistics import check_seasonality, plot_acf\n",
    "from darts.datasets import AirPassengersDataset, SunspotsDataset\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrent Models\n",
    "\n",
    "Darts includes two recurrent forecasting model classes: `RNNModel` and `BlockRNNModel`. \n",
    "\n",
    "`RNNModel` is fully recurrent in the sense that, at prediction time, an output is computed using these inputs:\n",
    "\n",
    "- the previous target value, which will be set to the last known target value for the first prediction,\n",
    "  and for all other predictions it will be set to the previous prediction\n",
    "- the previous hidden state\n",
    "- the current covariates (if the model was trained with covariates)\n",
    "\n",
    "A prediction with forecasting horizon `n` thus is created in `n` iterations of `RNNModel` predictions and requires `n` future covariates to be known. This model is suited for forecasting problems where the target series is highly dependent on covariates that are known in advance.\n",
    "\n",
    "`BlockRNNModel` has a recurrent encoder stage, which encodes its input, and a fully-connected neural network decoder stage, which produces a prediction of length `output_chunk_length` based on the last hidden state of the encoder stage. Consequently, this model produces 'blocks' of forecasts and is restricted to looking at covariates with the same time index as the input target series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Air Passenger Example\n",
    "This is a data set that is highly dependent on covariates. Knowing the month tells us a lot about the seasonal component, whereas the year determines the effect of the trend component. Both of these covariates are known in the future, and thus the `RNNModel` class is the preferred choice for this problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data:\n",
    "series = AirPassengersDataset().load()\n",
    "\n",
    "# Create training and validation sets:\n",
    "train, val = series.split_after(pd.Timestamp(\"19590101\"))\n",
    "\n",
    "# Normalize the time series (note: we avoid fitting the transformer on the validation set)\n",
    "transformer = Scaler()\n",
    "train_transformed = transformer.fit_transform(train)\n",
    "val_transformed = transformer.transform(val)\n",
    "series_transformed = transformer.transform(series)\n",
    "\n",
    "# create month and year covariate series\n",
    "year_series = datetime_attribute_timeseries(\n",
    "    pd.date_range(start=series.start_time(), freq=series.freq_str, periods=1000),\n",
    "    attribute=\"year\",\n",
    "    one_hot=False,\n",
    ")\n",
    "year_series = Scaler().fit_transform(year_series)\n",
    "month_series = datetime_attribute_timeseries(\n",
    "    year_series, attribute=\"month\", one_hot=True\n",
    ")\n",
    "covariates = year_series.stack(month_series)\n",
    "cov_train, cov_val = covariates.split_after(pd.Timestamp(\"19590101\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train an LSTM neural net. For using vanilla RNN or GRU instead, replace `'LSTM'` by `'RNN'` or `'GRU'`, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "my_model = RNNModel(\n",
    "    model=\"LSTM\",\n",
    "    hidden_dim=20,\n",
    "    dropout=0,\n",
    "    batch_size=16,\n",
    "    n_epochs=300,\n",
    "    optimizer_kwargs={\"lr\": 1e-3},\n",
    "    model_name=\"Air_RNN\",\n",
    "    log_tensorboard=True,\n",
    "    random_state=42,\n",
    "    training_length=20,\n",
    "    input_chunk_length=14,\n",
    "    force_reset=True,\n",
    "    save_checkpoints=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what follows, we can just provide the whole `covariates` series as `future_covariates` argument to the model; the model will slice these covariates and use only what it needs in order to train on forecasting the target `train_transformed`:"
   ]
  },
  {
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      "text/plain": [
       "<darts.models.forecasting.rnn_model.RNNModel at 0x7f800691edc0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_model.fit(\n",
    "    train_transformed,\n",
    "    future_covariates=covariates,\n",
    "    val_series=val_transformed,\n",
    "    val_future_covariates=covariates,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at predictions on the validation set\n",
    "Use the \"current\" model - i.e., the model at the end of the training procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a33c7d1d85da426584bf882f276e51b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def eval_model(model):\n",
    "    pred_series = model.predict(n=26, future_covariates=covariates)\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    series_transformed.plot(label=\"actual\")\n",
    "    pred_series.plot(label=\"forecast\")\n",
    "    plt.title(\"MAPE: {:.2f}%\".format(mape(pred_series, val_transformed)))\n",
    "    plt.legend()\n",
    "\n",
    "\n",
    "eval_model(my_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the best model obtained over training, according to validation loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b01dce8a78ef497bb542d72c922e4431",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = RNNModel.load_from_checkpoint(model_name=\"Air_RNN\", best=True)\n",
    "eval_model(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backtesting\n",
    "Let's backtest our `RNN` model, to see how it performs at a forecast horizon of 6 months:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59064c4ceaa24ab0b44afd53718a59d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "013cbbfa9a134e419722bf4fb98cf985",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ffd339c986a4c9e880b7207fcefdc62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19a8f638a7ca4e578b34eb356669300e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "99d1b37cef934b61891062243e5805d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b34a1d262cea433fa6556d9c75880dbb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "867013b1d08746918a520bc50507d80a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bab31622c2445719c92ebe9da584af5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cb22e61894be46c19b3f1988c916fdf7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c23cada5ec444465950abfe082c5cd42",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69cb5acaebfc43328e56db46a92e2b5a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6ffe115486a4682b3ae78e5513a7d63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5234c33f9eca42e2bb2f00cc804d90db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "682b951491124d8894145e6bcb6efe29",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63a4e943a3b5402e85a780a2b350ba39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd3e8f386cc8483ba502f2c9228a0ea4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afc5106a08244cf7914c81449f27e228",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5c83c6b2da648e3a3e4096db0e2550b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfe40f93a0134c7a916bab3615ef4a39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08f31785638f48c38c89526c3c3d301b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "backtest_series = my_model.historical_forecasts(\n",
    "    series_transformed,\n",
    "    future_covariates=covariates,\n",
    "    start=pd.Timestamp(\"19590101\"),\n",
    "    forecast_horizon=6,\n",
    "    retrain=False,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 2.71%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "series_transformed.plot(label=\"actual\")\n",
    "backtest_series.plot(label=\"backtest\")\n",
    "plt.legend()\n",
    "plt.title(\"Backtest, starting Jan 1959, 6-months horizon\")\n",
    "print(\n",
    "    \"MAPE: {:.2f}%\".format(\n",
    "        mape(\n",
    "            transformer.inverse_transform(series_transformed),\n",
    "            transformer.inverse_transform(backtest_series),\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly sunspots\n",
    "Let's now try a more challenging time series; that of the monthly number of sunspots since 1749. First, we build the time series from the data, and check its periodicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 125)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_sunspot = SunspotsDataset().load()\n",
    "\n",
    "series_sunspot.plot()\n",
    "check_seasonality(series_sunspot, max_lag=240)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(series_sunspot, 125, max_lag=240)  # ~11 years seasonality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sp, val_sp = series_sunspot.split_after(pd.Timestamp(\"19401001\"))\n",
    "\n",
    "transformer_sunspot = Scaler()\n",
    "train_sp_transformed = transformer_sunspot.fit_transform(train_sp)\n",
    "val_sp_transformed = transformer_sunspot.transform(val_sp)\n",
    "series_sp_transformed = transformer_sunspot.transform(series_sunspot)"
   ]
  },
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   "execution_count": 14,
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    "my_model_sun = BlockRNNModel(\n",
    "    model=\"GRU\",\n",
    "    input_chunk_length=125,\n",
    "    output_chunk_length=36,\n",
    "    hidden_dim=10,\n",
    "    n_rnn_layers=1,\n",
    "    batch_size=32,\n",
    "    n_epochs=100,\n",
    "    dropout=0.1,\n",
    "    model_name=\"sun_GRU\",\n",
    "    nr_epochs_val_period=1,\n",
    "    optimizer_kwargs={\"lr\": 1e-3},\n",
    "    log_tensorboard=True,\n",
    "    random_state=42,\n",
    "    force_reset=True,\n",
    ")\n",
    "\n",
    "my_model_sun.fit(train_sp_transformed, val_series=val_sp_transformed, verbose=True)"
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   "source": [
    "To evaluate our model, we will simulate historic forecasting with a forecasting horizon of 3 years across the validation set. To speed things up, we will only look at every 10th forecast. For the sake of comparison, let's also fit an exponential smoothing model."
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   ],
   "source": [
    "# Compute the backtest predictions with the two models\n",
    "pred_series = my_model_sun.historical_forecasts(\n",
    "    series_sp_transformed,\n",
    "    start=pd.Timestamp(\"19401001\"),\n",
    "    forecast_horizon=36,\n",
    "    stride=10,\n",
    "    retrain=False,\n",
    "    last_points_only=True,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "pred_series_ets = ExponentialSmoothing(seasonal_periods=120).historical_forecasts(\n",
    "    series_sp_transformed,\n",
    "    start=pd.Timestamp(\"19401001\"),\n",
    "    forecast_horizon=36,\n",
    "    stride=10,\n",
    "    retrain=True,\n",
    "    last_points_only=True,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RNN MAPE: 73.19010018398568\n",
      "ETS MAPE: 116.63584309419007\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_sp_transformed.plot(label=\"actual\")\n",
    "pred_series.plot(label=\"our RNN\")\n",
    "pred_series_ets.plot(label=\"ETS\")\n",
    "plt.legend()\n",
    "print(\"RNN MAPE:\", mape(pred_series, val_sp_transformed))\n",
    "print(\"ETS MAPE:\", mape(pred_series_ets, val_sp_transformed))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
